This research contributes to the existing corporate governance (CG) and social and environmental accountability (SEA) literature by exploring the impact of CG mechanisms (board independence, board size, CEO duality, and board gender diversity) on Chinese firms’ environmental performance, sustainability performance, and environmental information disclosures (EID). Furthermore, the investigation consequently ascertains the amount to which the CG–SEA connection is influenced by CEO qualities. Using a dynamic model of a SysGMM regression model, we found that board size, independence, and gender diversity in board and CEO duality are all favorably connected to Chinese enterprises’ environmental performance over a window of 10 years (2010–2019). Additionally, our findings imply that the analyzed CEO characteristics positively moderate the relationship between CG and SEA. Our findings have significant consequences for all stakeholders, including environmentalists, corporate regulators, CEOs, policymakers, and regulators.
We study the nexus between environmental, social, and governance (ESG) performance and corporate capital financing decisions. Further, we also analyze the effect of audit quality and type of ownership (state-owned enterprises (SOEs) vs non-state-owned enterprises (non-SOEs), local vs central SOEs in this relationship. By applying panel regression (fixed effects) on 6295 firm-year observations of Chinese A-listed enterprises data for 2010–2019, we conclude that firms’ ESG information is crucial to their financing decisions. In particular, firms with superior ESG performance have lower debt financing. The findings suggest that enterprises with strong ESG performance have easy access to equity funding via stock markets. Further, this relationship is more pronounced in SOE compared to non-SOEs and in central SOEs compared to local SOEs. These results demonstrate that the market may promote desired social outcomes by rewarding ESG performance; however, we find no significant effect of audit quality in this relationship. Findings are robust to different sensitivity tests, including an alternative estimation, sysGMM regression to address endogeneity issues, and lagged regressions to address reverse causality.
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